Therefore, we systematically address these problems.
Particularly,
blackLearning-based Edges (L-Edges) are advocated as the important representations for meaningful holistic structures.
A novel transformer-based model is presented to restore holistic structures by blackL-Edges, wireframe lines and gradients.
Critically, we further iteratively upsample to arbitrary resolution the grayscale L-Edges by Edge Non-Maximum Suppression (E-NMS), and lines by a simple CNN, respectively. This greatly benefits the structural prior learned in LR images, adaptive to the inpainting in HR images.
A novel and well designed positional encoding scheme for the mask region is utilized to improve the performance of image restoration.
Further,
black
Fourier convolutions from [1]} have been employed as the backbone for texture restoration. We significantly improve it with Large Kernel Attention (LKA) modules [2]}, as our FTR module.
Typically, FTR can be independently pre-trained as in [1]}.
 Furthermore,
to reduce the computational cost, we propose a novel incrementally training strategy with Zero-initialized Residual Addition (ZeroRA) [4]} to incorporate structural prior into a pre-trained FTR module.
This incremental strategy enjoys fast convergence for much fewer steps compared with retraining a model from the scratch.
